{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:33:53.416002Z",
     "start_time": "2024-11-09T11:33:50.447715Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import torch\n",
    "from matplotlib import pyplot\n",
    "from numpy import vstack\n",
    "from numpy import argmax\n",
    "from pandas import read_csv\n",
    "from sklearn.metrics import accuracy_score\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision.transforms import Compose\n",
    "from torchvision.transforms import ToTensor\n",
    "from torchvision.transforms import Normalize\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.nn import Conv2d\n",
    "from torch.nn import MaxPool2d\n",
    "from torch.nn import Linear\n",
    "from torch.nn import ReLU\n",
    "from torch.nn import Softmax\n",
    "from torch.nn import Module\n",
    "from torch.optim import SGD\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.nn.init import kaiming_uniform_\n",
    "from torch.nn.init import xavier_uniform_\n",
    " \n",
    "# 模型定义\n",
    "class CNN(Module):\n",
    "    # 定义模型属性\n",
    "    def __init__(self, n_channels):\n",
    "        super(CNN, self).__init__()\n",
    "        # 输入到隐层 1\n",
    "        self.hidden1 = Conv2d(n_channels, 32, (3,3))\n",
    "        kaiming_uniform_(self.hidden1.weight, nonlinearity='relu')\n",
    "        self.act1 = ReLU()\n",
    "        # 池化层 1\n",
    "        self.pool1 = MaxPool2d((2,2), stride=(2,2))\n",
    "        # 隐层 2\n",
    "        self.hidden2 = Conv2d(32, 32, (3,3))\n",
    "        kaiming_uniform_(self.hidden2.weight, nonlinearity='relu')\n",
    "        self.act2 = ReLU()\n",
    "        # 池化层 2\n",
    "        self.pool2 = MaxPool2d((2,2), stride=(2,2))\n",
    "        # 全连接层\n",
    "        self.hidden3 = Linear(5*5*32, 100)\n",
    "        kaiming_uniform_(self.hidden3.weight, nonlinearity='relu')\n",
    "        self.act3 = ReLU()\n",
    "        # 输出层\n",
    "        self.hidden4 = Linear(100, 10)\n",
    "        xavier_uniform_(self.hidden4.weight)\n",
    "        self.act4 = Softmax(dim=1)\n",
    " \n",
    "    # 前向传播\n",
    "    def forward(self, X):\n",
    "        # 输入到隐层 1\n",
    "        X = self.hidden1(X)\n",
    "        X = self.act1(X)\n",
    "        X = self.pool1(X)\n",
    "        # 隐层 2\n",
    "        X = self.hidden2(X)\n",
    "        X = self.act2(X)\n",
    "        X = self.pool2(X)\n",
    "        # 扁平化\n",
    "        X = X.view(-1, 4*4*50)\n",
    "        # 隐层 3\n",
    "        X = self.hidden3(X)\n",
    "        X = self.act3(X)\n",
    "        # 输出层\n",
    "        X = self.hidden4(X)\n",
    "        X = self.act4(X)\n",
    "        return X\n",
    " \n",
    "# 准备数据集\n",
    "def prepare_data(path):\n",
    "    # 定义标准化，0.1307，0.3081为MNIST数据的均值和标准差\n",
    "    trans = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])\n",
    "    # 加载数据集\n",
    "    train = MNIST(path, train=True, download=True, transform=trans)# 自动下载训练数据集、测试数据集\n",
    "    test = MNIST(path, train=False, download=True, transform=trans)\n",
    "    # 为训练集和测试集创建 DataLoader\n",
    "    train_dl = DataLoader(train, batch_size=64, shuffle=True)\n",
    "    test_dl = DataLoader(test, batch_size=1024, shuffle=False)\n",
    "    # 以 batch 方式获取图片\n",
    "    i, (inputs, targets) = next(enumerate(train_dl))\n",
    "    # 绘图\n",
    "    for i in range(25):\n",
    "        # 定义子图\n",
    "        pyplot.subplot(5, 5, i+1)\n",
    "        # 绘制原始像素数据\n",
    "        pyplot.imshow(inputs[i][0], cmap='gray')\n",
    "    print(\"Data loading...\")\n",
    "    # 展示图片\n",
    "    pyplot.show()\n",
    "    return train_dl, test_dl\n",
    " \n",
    "# 训练模型\n",
    "def train_model(train_dl, model):\n",
    "    # 定义优化器\n",
    "    criterion = CrossEntropyLoss()\n",
    "    optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
    "    # 枚举 epochs\n",
    "    for epoch in range(10):\n",
    "        # 枚举 mini batches\n",
    "        # print(\"Train Epoch: %d\" % epoch)\n",
    "        for i, (inputs, targets) in enumerate(train_dl):\n",
    "            # 梯度清除\n",
    "            optimizer.zero_grad()\n",
    "            # 计算模型输出\n",
    "            yhat = model(inputs)\n",
    "            # 计算损失\n",
    "            loss = criterion(yhat, targets)\n",
    "            # 贡献度分配\n",
    "            loss.backward()\n",
    "            # 升级模型权重\n",
    "            optimizer.step()\n",
    "            if i % 200 == 0:\n",
    "                print('Train Epoch: {} [{:5d}/{} ({:2.0f}%)]'.format(epoch, i * len(inputs),len(train_dl.dataset),100. * i / len(train_dl)))\n",
    " \n",
    "# 评估模型\n",
    "def evaluate_model(test_dl, model):\n",
    "    predictions, actuals = list(), list()\n",
    "    for i, (inputs, targets) in enumerate(test_dl):\n",
    "        # 在测试集上评估模型\n",
    "        yhat = model(inputs)\n",
    "        # 转化为 numpy 数据类型\n",
    "        yhat = yhat.detach().numpy()\n",
    "        actual = targets.numpy()\n",
    "        # 转化为类标签\n",
    "        yhat = argmax(yhat, axis=1)\n",
    "        # 为 stack 格式化数据集\n",
    "        actual = actual.reshape((len(actual), 1))\n",
    "        yhat = yhat.reshape((len(yhat), 1))\n",
    "        # 保存\n",
    "        predictions.append(yhat)\n",
    "        actuals.append(actual)\n",
    "        \n",
    "    predictions, actuals = vstack(predictions), vstack(actuals)\n",
    "    # 计算准确度\n",
    "    acc = accuracy_score(actuals, predictions)\n",
    "    return acc"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:33:54.108486Z",
     "start_time": "2024-11-09T11:33:53.416002Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 准备数据\n",
    "path = os.path.join(os.getcwd(), 'datasets/mnist')\n",
    "train_dl, test_dl = prepare_data(path)\n",
    "print(\"Number of training samples:\", len(train_dl.dataset))\n",
    "print(\"Number of test samples:\", len(test_dl.dataset))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loading...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 25 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 60000\n",
      "Number of test samples: 10000\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:33:54.173305Z",
     "start_time": "2024-11-09T11:33:54.165283Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 定义网络\n",
    "model = CNN(1)\n",
    "print(\"CNN Network Definition Complete\")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Network Definition Complete\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:36:43.599997Z",
     "start_time": "2024-11-09T11:33:54.181631Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练模型\n",
    "print(\"Training starts\")\n",
    "train_model(train_dl, model) \n",
    "print(\"CNN Network Training Complete\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training starts\n",
      "Train Epoch: 0 [    0/60000 ( 0%)]\n",
      "Train Epoch: 0 [12800/60000 (21%)]\n",
      "Train Epoch: 0 [25600/60000 (43%)]\n",
      "Train Epoch: 0 [38400/60000 (64%)]\n",
      "Train Epoch: 0 [51200/60000 (85%)]\n",
      "Train Epoch: 1 [    0/60000 ( 0%)]\n",
      "Train Epoch: 1 [12800/60000 (21%)]\n",
      "Train Epoch: 1 [25600/60000 (43%)]\n",
      "Train Epoch: 1 [38400/60000 (64%)]\n",
      "Train Epoch: 1 [51200/60000 (85%)]\n",
      "Train Epoch: 2 [    0/60000 ( 0%)]\n",
      "Train Epoch: 2 [12800/60000 (21%)]\n",
      "Train Epoch: 2 [25600/60000 (43%)]\n",
      "Train Epoch: 2 [38400/60000 (64%)]\n",
      "Train Epoch: 2 [51200/60000 (85%)]\n",
      "Train Epoch: 3 [    0/60000 ( 0%)]\n",
      "Train Epoch: 3 [12800/60000 (21%)]\n",
      "Train Epoch: 3 [25600/60000 (43%)]\n",
      "Train Epoch: 3 [38400/60000 (64%)]\n",
      "Train Epoch: 3 [51200/60000 (85%)]\n",
      "Train Epoch: 4 [    0/60000 ( 0%)]\n",
      "Train Epoch: 4 [12800/60000 (21%)]\n",
      "Train Epoch: 4 [25600/60000 (43%)]\n",
      "Train Epoch: 4 [38400/60000 (64%)]\n",
      "Train Epoch: 4 [51200/60000 (85%)]\n",
      "Train Epoch: 5 [    0/60000 ( 0%)]\n",
      "Train Epoch: 5 [12800/60000 (21%)]\n",
      "Train Epoch: 5 [25600/60000 (43%)]\n",
      "Train Epoch: 5 [38400/60000 (64%)]\n",
      "Train Epoch: 5 [51200/60000 (85%)]\n",
      "Train Epoch: 6 [    0/60000 ( 0%)]\n",
      "Train Epoch: 6 [12800/60000 (21%)]\n",
      "Train Epoch: 6 [25600/60000 (43%)]\n",
      "Train Epoch: 6 [38400/60000 (64%)]\n",
      "Train Epoch: 6 [51200/60000 (85%)]\n",
      "Train Epoch: 7 [    0/60000 ( 0%)]\n",
      "Train Epoch: 7 [12800/60000 (21%)]\n",
      "Train Epoch: 7 [25600/60000 (43%)]\n",
      "Train Epoch: 7 [38400/60000 (64%)]\n",
      "Train Epoch: 7 [51200/60000 (85%)]\n",
      "Train Epoch: 8 [    0/60000 ( 0%)]\n",
      "Train Epoch: 8 [12800/60000 (21%)]\n",
      "Train Epoch: 8 [25600/60000 (43%)]\n",
      "Train Epoch: 8 [38400/60000 (64%)]\n",
      "Train Epoch: 8 [51200/60000 (85%)]\n",
      "Train Epoch: 9 [    0/60000 ( 0%)]\n",
      "Train Epoch: 9 [12800/60000 (21%)]\n",
      "Train Epoch: 9 [25600/60000 (43%)]\n",
      "Train Epoch: 9 [38400/60000 (64%)]\n",
      "Train Epoch: 9 [51200/60000 (85%)]\n",
      "CNN Network Training Complete\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:36:44.654760Z",
     "start_time": "2024-11-09T11:36:43.604490Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 评估模型\n",
    "acc = evaluate_model(test_dl, model)\n",
    "print('CNN Network accuracy: %.3f%%' % (acc * 100))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN Network accuracy: 98.850%\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:36:44.739402Z",
     "start_time": "2024-11-09T11:36:44.725930Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 导出模型\n",
    "model_path = os.path.join(os.getcwd(), 'model_path/cnn_model.pth')\n",
    "torch.save(model.state_dict(), model_path)\n",
    "print(f'Model successfully saved to {model_path}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model successfully saved to C:\\Users\\ASUS\\Documents\\CodeCargo\\ezCNN\\model_path/cnn_model.pth\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-09T11:54:40.581806Z",
     "start_time": "2024-11-09T11:53:41.882151Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os  \n",
    "from matplotlib import pyplot  \n",
    "from numpy import vstack  \n",
    "from numpy import argmax  \n",
    "from pandas import read_csv  \n",
    "from sklearn.metrics import accuracy_score  \n",
    "from torchvision.datasets import MNIST  \n",
    "from torchvision.transforms import Compose, ToTensor, Normalize  \n",
    "from torch.utils.data import DataLoader  \n",
    "from torch.nn import Conv2d, MaxPool2d, Linear, ReLU, Softmax, Module  \n",
    "from torch.optim import SGD  \n",
    "from torch.nn import CrossEntropyLoss  \n",
    "from torch.nn.init import kaiming_uniform_, xavier_uniform_  \n",
    "import torch\n",
    "\n",
    "# 检查是否有GPU可用\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# 模型定义  \n",
    "class CNN(Module):  \n",
    "    # 定义模型属性  \n",
    "    def __init__(self, n_channels):  \n",
    "        super(CNN, self).__init__()  \n",
    "        self.hidden1 = Conv2d(n_channels, 32, (3,3))  \n",
    "        kaiming_uniform_(self.hidden1.weight, nonlinearity='relu')  \n",
    "        self.act1 = ReLU()  \n",
    "        self.pool1 = MaxPool2d((2,2), stride=(2,2))  \n",
    "        self.hidden2 = Conv2d(32, 32, (3,3))  \n",
    "        kaiming_uniform_(self.hidden2.weight, nonlinearity='relu')  \n",
    "        self.act2 = ReLU()  \n",
    "        self.pool2 = MaxPool2d((2,2), stride=(2,2))  \n",
    "        self.hidden3 = Linear(5*5*32, 100)  \n",
    "        kaiming_uniform_(self.hidden3.weight, nonlinearity='relu')  \n",
    "        self.act3 = ReLU()  \n",
    "        self.hidden4 = Linear(100, 10)  \n",
    "        xavier_uniform_(self.hidden4.weight)  \n",
    "        self.act4 = Softmax(dim=1)  \n",
    "\n",
    "    # 前向传播  \n",
    "    def forward(self, X):  \n",
    "        X = self.hidden1(X)  \n",
    "        X = self.act1(X)  \n",
    "        X = self.pool1(X)  \n",
    "        X = self.hidden2(X)  \n",
    "        X = self.act2(X)  \n",
    "        X = self.pool2(X)  \n",
    "        X = X.view(-1, 5*5*32)  \n",
    "        X = self.hidden3(X)  \n",
    "        X = self.act3(X)  \n",
    "        X = self.hidden4(X)  \n",
    "        X = self.act4(X)  \n",
    "        return X  \n",
    "\n",
    "# 准备数据集  \n",
    "def prepare_data(path):  \n",
    "    trans = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])  \n",
    "    train = MNIST(path, train=True, download=True, transform=trans)  \n",
    "    test = MNIST(path, train=False, download=True, transform=trans)  \n",
    "    train_dl = DataLoader(train, batch_size=64, shuffle=True)  \n",
    "    test_dl = DataLoader(test, batch_size=1024, shuffle=False)  \n",
    "    i, (inputs, targets) = next(enumerate(train_dl))  \n",
    "    for i in range(25):  \n",
    "        pyplot.subplot(5, 5, i+1)  \n",
    "        pyplot.imshow(inputs[i][0], cmap='gray')  \n",
    "    print(\"Data loading...\")  \n",
    "    pyplot.show()  \n",
    "    return train_dl, test_dl  \n",
    "\n",
    "# 训练模型  \n",
    "def train_model(train_dl, model):  \n",
    "    criterion = CrossEntropyLoss()  \n",
    "    optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)  \n",
    "    for epoch in range(10):  \n",
    "        for i, (inputs, targets) in enumerate(train_dl):  \n",
    "            inputs, targets = inputs.to(device), targets.to(device)  # 将数据转移到GPU\n",
    "            optimizer.zero_grad()  \n",
    "            yhat = model(inputs)  \n",
    "            loss = criterion(yhat, targets)  \n",
    "            loss.backward()  \n",
    "            optimizer.step()  \n",
    "\n",
    "# 评估模型  \n",
    "def evaluate_model(test_dl, model):  \n",
    "    predictions, actuals = list(), list()  \n",
    "    for i, (inputs, targets) in enumerate(test_dl):  \n",
    "        inputs, targets = inputs.to(device), targets.to(device)  # 将数据转移到GPU\n",
    "        yhat = model(inputs)  \n",
    "        yhat = yhat.detach().cpu().numpy()  # 将结果转移回CPU进行计算  \n",
    "        actual = targets.cpu().numpy()  \n",
    "        yhat = argmax(yhat, axis=1)  \n",
    "        actual = actual.reshape((len(actual), 1))  \n",
    "        yhat = yhat.reshape((len(yhat), 1))  \n",
    "        predictions.append(yhat)  \n",
    "        actuals.append(actual)  \n",
    "          \n",
    "    predictions, actuals = vstack(predictions), vstack(actuals)  \n",
    "    acc = accuracy_score(actuals, predictions)  \n",
    "    return acc  \n",
    "\n",
    "# 准备数据  \n",
    "path = os.path.join(os.getcwd(), 'datasets/mnist')  \n",
    "train_dl, test_dl = prepare_data(path)  \n",
    "print(\"Number of training samples:\", len(train_dl.dataset))  \n",
    "print(\"Number of test samples:\", len(test_dl.dataset))  \n",
    "\n",
    "# 定义网络并移动到GPU\n",
    "model = CNN(1).to(device)\n",
    "print(\"CNN Network Definition Complete\")  \n",
    "\n",
    "# 训练模型  \n",
    "print(\"Model training...\")  \n",
    "train_model(train_dl, model)  \n",
    "print(\"CNN Network Training Complete\")  \n",
    "\n",
    "# 评估模型  \n",
    "acc = evaluate_model(test_dl, model)  \n",
    "print('CNN Network accuracy: %.3f' % acc)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loading...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 25 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 60000\n",
      "Number of test samples: 10000\n",
      "CNN Network Definition Complete\n",
      "Model training...\n",
      "CNN Network Training Complete\n",
      "CNN Network accuracy: 0.988\n"
     ]
    }
   ],
   "execution_count": 1
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
